from sklearn_benchmarks.reporting.hp_match import HPMatchReporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

scikit-learn vs. scikit-learn-intelex (Intel® oneAPI)

reporting = HPMatchReporting("sklearnex", config="config.yml")
reporting.make_report()

KNeighborsClassifier_brute_force

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

All estimators share the following parameters: algorithm=brute.

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_sklearnex	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.014	0.001	NaN	5.631	0.0	-1	1	NaN	NaN	0.059	0.005	0.241	0.242	See	See
3	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.014	0.001	NaN	5.613	0.0	-1	5	NaN	NaN	0.057	0.001	0.249	0.249	See	See
6	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.014	0.001	NaN	5.692	0.0	1	100	NaN	NaN	0.057	0.001	0.248	0.248	See	See
9	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.014	0.001	NaN	5.738	0.0	-1	100	NaN	NaN	0.057	0.002	0.244	0.244	See	See
12	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.014	0.001	NaN	5.576	0.0	1	5	NaN	NaN	0.057	0.001	0.252	0.252	See	See
15	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.015	0.001	NaN	5.267	0.0	1	1	NaN	NaN	0.057	0.001	0.268	0.268	See	See
18	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.000	NaN	0.330	0.0	-1	1	NaN	NaN	0.009	0.000	0.554	0.555	See	See
21	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.000	NaN	0.300	0.0	-1	5	NaN	NaN	0.010	0.000	0.562	0.562	See	See
24	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.000	NaN	0.332	0.0	1	100	NaN	NaN	0.009	0.001	0.514	0.515	See	See
27	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.000	NaN	0.337	0.0	-1	100	NaN	NaN	0.010	0.001	0.499	0.500	See	See
30	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.000	NaN	0.326	0.0	1	5	NaN	NaN	0.009	0.000	0.537	0.538	See	See
33	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.001	NaN	0.339	0.0	1	1	NaN	NaN	0.010	0.001	0.483	0.485	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_sklearnex	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.287	0.096	NaN	0.0	0.002	-1	1	0.663	0.687	0.472	0.023	4.849	4.854	See	See
2	KNeighborsClassifier_brute_force	predict	100000	1	100	0.027	0.003	NaN	0.0	0.027	-1	1	1.000	1.000	0.012	0.001	2.341	2.350	See	See
4	KNeighborsClassifier_brute_force	predict	100000	1000	100	3.151	0.049	NaN	0.0	0.003	-1	5	0.757	0.742	0.466	0.009	6.759	6.760	See	See
5	KNeighborsClassifier_brute_force	predict	100000	1	100	0.029	0.002	NaN	0.0	0.029	-1	5	1.000	1.000	0.011	0.001	2.642	2.646	See	See
7	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.536	0.050	NaN	0.0	0.003	1	100	0.882	0.875	0.521	0.004	4.865	4.865	See	See
8	KNeighborsClassifier_brute_force	predict	100000	1	100	0.028	0.004	NaN	0.0	0.028	1	100	1.000	0.000	0.013	0.003	2.120	2.160	See	See
10	KNeighborsClassifier_brute_force	predict	100000	1000	100	3.182	0.056	NaN	0.0	0.003	-1	100	0.882	0.875	0.519	0.006	6.129	6.129	See	See
11	KNeighborsClassifier_brute_force	predict	100000	1	100	0.028	0.003	NaN	0.0	0.028	-1	100	1.000	0.000	0.012	0.002	2.364	2.390	See	See
13	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.518	0.027	NaN	0.0	0.003	1	5	0.757	0.742	0.460	0.006	5.470	5.470	See	See
14	KNeighborsClassifier_brute_force	predict	100000	1	100	0.026	0.001	NaN	0.0	0.026	1	5	1.000	1.000	0.011	0.001	2.311	2.325	See	See
16	KNeighborsClassifier_brute_force	predict	100000	1000	100	1.626	0.013	NaN	0.0	0.002	1	1	0.663	0.687	0.467	0.010	3.481	3.482	See	See
17	KNeighborsClassifier_brute_force	predict	100000	1	100	0.025	0.001	NaN	0.0	0.025	1	1	1.000	1.000	0.011	0.000	2.271	2.273	See	See
19	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.839	0.051	NaN	0.0	0.002	-1	1	0.896	0.967	0.103	0.004	17.911	17.924	See	See
20	KNeighborsClassifier_brute_force	predict	100000	1	2	0.007	0.002	NaN	0.0	0.007	-1	1	1.000	1.000	0.001	0.000	10.524	10.585	See	See
22	KNeighborsClassifier_brute_force	predict	100000	1000	2	2.782	0.028	NaN	0.0	0.003	-1	5	0.922	0.974	0.104	0.001	26.876	26.878	See	See
23	KNeighborsClassifier_brute_force	predict	100000	1	2	0.009	0.005	NaN	0.0	0.009	-1	5	1.000	1.000	0.001	0.000	12.055	12.366	See	See
25	KNeighborsClassifier_brute_force	predict	100000	1000	2	2.210	0.012	NaN	0.0	0.002	1	100	0.929	0.975	0.161	0.003	13.701	13.703	See	See
26	KNeighborsClassifier_brute_force	predict	100000	1	2	0.003	0.000	NaN	0.0	0.003	1	100	1.000	1.000	0.001	0.000	4.469	4.520	See	See
28	KNeighborsClassifier_brute_force	predict	100000	1000	2	2.816	0.036	NaN	0.0	0.003	-1	100	0.929	0.975	0.170	0.012	16.519	16.561	See	See
29	KNeighborsClassifier_brute_force	predict	100000	1	2	0.008	0.003	NaN	0.0	0.008	-1	100	1.000	1.000	0.001	0.000	10.113	10.153	See	See
31	KNeighborsClassifier_brute_force	predict	100000	1000	2	2.207	0.016	NaN	0.0	0.002	1	5	0.922	0.974	0.106	0.003	20.905	20.912	See	See
32	KNeighborsClassifier_brute_force	predict	100000	1	2	0.003	0.000	NaN	0.0	0.003	1	5	1.000	1.000	0.001	0.000	5.072	5.139	See	See
34	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.308	0.018	NaN	0.0	0.001	1	1	0.896	0.967	0.102	0.002	12.830	12.832	See	See
35	KNeighborsClassifier_brute_force	predict	100000	1	2	0.002	0.000	NaN	0.0	0.002	1	1	1.000	1.000	0.001	0.000	3.290	3.323	See	See

KNeighborsClassifier_kd_tree

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

All estimators share the following parameters: algorithm=kd_tree.

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_sklearnex	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.087	0.059	NaN	0.026	0.0	-1	1	NaN	NaN	0.981	0.287	3.147	3.279	See	See
3	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.938	0.060	NaN	0.020	0.0	-1	5	NaN	NaN	0.883	0.012	4.459	4.459	See	See
6	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.881	0.107	NaN	0.021	0.0	1	100	NaN	NaN	0.889	0.010	4.368	4.368	See	See
9	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.896	0.059	NaN	0.021	0.0	-1	100	NaN	NaN	0.900	0.024	4.331	4.333	See	See
12	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.845	0.070	NaN	0.021	0.0	1	5	NaN	NaN	0.893	0.013	4.305	4.305	See	See
15	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.899	0.123	NaN	0.021	0.0	1	1	NaN	NaN	0.904	0.018	4.314	4.314	See	See
18	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.001	NaN	0.015	0.0	-1	1	NaN	NaN	0.005	0.003	0.231	0.286	See	See
21	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	NaN	0.023	0.0	-1	5	NaN	NaN	0.002	0.001	0.453	0.589	See	See
24	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	NaN	0.025	0.0	1	100	NaN	NaN	0.002	0.001	0.356	0.435	See	See
27	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	NaN	0.026	0.0	-1	100	NaN	NaN	0.001	0.000	0.570	0.571	See	See
30	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	NaN	0.024	0.0	1	5	NaN	NaN	0.001	0.000	0.606	0.607	See	See
33	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	NaN	0.025	0.0	1	1	NaN	NaN	0.001	0.000	0.595	0.596	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_sklearnex	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.987	1.294	NaN	0.000	0.001	-1	1	0.929	0.910	0.128	0.006	7.743	7.751	See	See
2	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.003	0.001	NaN	0.000	0.003	-1	1	1.000	1.000	0.000	0.000	10.580	10.853	See	See
4	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	1.147	0.474	NaN	0.000	0.001	-1	5	0.946	0.941	0.232	0.004	4.952	4.953	See	See
5	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.004	0.001	NaN	0.000	0.004	-1	5	1.000	1.000	0.001	0.000	6.598	6.810	See	See
7	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	5.905	0.750	NaN	0.000	0.006	1	100	0.951	0.940	0.708	0.014	8.336	8.338	See	See
8	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.003	0.001	NaN	0.000	0.003	1	100	1.000	1.000	0.001	0.001	2.724	3.193	See	See
10	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	3.249	0.417	NaN	0.000	0.003	-1	100	0.951	0.940	0.708	0.011	4.586	4.587	See	See
11	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.005	0.001	NaN	0.000	0.005	-1	100	1.000	1.000	0.001	0.001	4.910	5.441	See	See
13	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	1.827	0.469	NaN	0.000	0.002	1	5	0.946	0.941	0.243	0.011	7.519	7.526	See	See
14	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.002	0.000	NaN	0.000	0.002	1	5	1.000	1.000	0.001	0.000	2.966	3.089	See	See
16	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	1.016	0.402	NaN	0.000	0.001	1	1	0.929	0.910	0.131	0.005	7.737	7.742	See	See
17	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.001	0.000	NaN	0.000	0.001	1	1	1.000	1.000	0.000	0.000	3.508	3.653	See	See
19	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.040	0.016	NaN	0.000	0.000	-1	1	0.891	0.879	0.001	0.000	67.926	68.156	See	See
20	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.003	0.000	NaN	0.000	0.003	-1	1	1.000	1.000	0.000	0.000	17.069	17.720	See	See
22	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.035	0.004	NaN	0.000	0.000	-1	5	0.911	0.905	0.001	0.000	38.489	38.708	See	See
23	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.003	0.000	NaN	0.000	0.003	-1	5	1.000	1.000	0.000	0.000	18.272	18.480	See	See
25	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.049	0.016	NaN	0.000	0.000	1	100	0.894	0.917	0.007	0.001	6.842	6.970	See	See
26	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	NaN	0.000	0.001	1	100	1.000	1.000	0.000	0.000	4.650	4.881	See	See
28	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.050	0.008	NaN	0.000	0.000	-1	100	0.894	0.917	0.007	0.002	7.503	7.739	See	See
29	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.003	0.000	NaN	0.000	0.003	-1	100	1.000	1.000	0.000	0.000	17.975	18.125	See	See
31	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.031	0.001	NaN	0.001	0.000	1	5	0.911	0.905	0.001	0.000	34.364	34.397	See	See
32	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	NaN	0.000	0.001	1	5	1.000	1.000	0.000	0.000	5.178	5.358	See	See
34	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.030	0.001	NaN	0.001	0.000	1	1	0.891	0.879	0.001	0.000	52.625	52.651	See	See
35	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	NaN	0.000	0.001	1	1	1.000	1.000	0.000	0.000	6.169	6.221	See	See

KMeans_tall

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

All estimators share the following parameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter_sklearn	iteration_throughput	latency	init	adjusted_rand_score_sklearn	n_iter_sklearnex	adjusted_rand_score_sklearnex	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_tall	fit	1000000	1000000	2	0.651	0.089	30	0.025	0.0	random	NaN	30	NaN	0.331	0.015	1.964	1.966	See	See
3	KMeans_tall	fit	1000000	1000000	2	0.697	0.018	30	0.023	0.0	k-means++	NaN	30	NaN	0.372	0.016	1.871	1.873	See	See
6	KMeans_tall	fit	1000000	1000000	100	7.811	0.257	30	0.102	0.0	random	NaN	30	NaN	4.299	0.092	1.817	1.818	See	See
9	KMeans_tall	fit	1000000	1000000	100	8.192	0.031	30	0.098	0.0	k-means++	NaN	30	NaN	4.493	0.033	1.823	1.823	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter_sklearn	iteration_throughput	latency	init	adjusted_rand_score_sklearn	n_iter_sklearnex	adjusted_rand_score_sklearnex	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_tall	predict	1000000	1000	2	0.002	0.001	30	0.007	0.000	random	0.001	30	0.001	0.0	0.0	8.870	11.128	See	See
2	KMeans_tall	predict	1000000	1	2	0.002	0.000	30	0.000	0.002	random	1.000	30	1.000	0.0	0.0	13.399	13.559	See	See
4	KMeans_tall	predict	1000000	1000	2	0.002	0.000	30	0.008	0.000	k-means++	0.001	30	0.001	0.0	0.0	8.274	8.507	See	See
5	KMeans_tall	predict	1000000	1	2	0.002	0.000	30	0.000	0.002	k-means++	1.000	30	1.000	0.0	0.0	10.205	10.757	See	See
7	KMeans_tall	predict	1000000	1000	100	0.002	0.000	30	0.380	0.000	random	0.001	30	0.002	0.0	0.0	6.389	6.724	See	See
8	KMeans_tall	predict	1000000	1	100	0.002	0.000	30	0.000	0.002	random	1.000	30	1.000	0.0	0.0	10.570	10.666	See	See
10	KMeans_tall	predict	1000000	1000	100	0.002	0.000	30	0.370	0.000	k-means++	0.002	30	0.002	0.0	0.0	6.461	6.688	See	See
11	KMeans_tall	predict	1000000	1	100	0.002	0.000	30	0.000	0.002	k-means++	1.000	30	1.000	0.0	0.0	9.962	10.100	See	See

KMeans_short

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

All estimators share the following parameters: algorithm=full, n_clusters=300, max_iter=20, n_init=1, tol=1e-16.

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter_sklearn	iteration_throughput	latency	init	adjusted_rand_score_sklearn	n_iter_sklearnex	adjusted_rand_score_sklearnex	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_short	fit	10000	10000	2	0.110	0.002	20	0.001	0.0	random	NaN	20	NaN	0.057	0.002	1.922	1.924	See	See
3	KMeans_short	fit	10000	10000	2	0.331	0.007	20	0.000	0.0	k-means++	NaN	20	NaN	0.147	0.002	2.257	2.257	See	See
6	KMeans_short	fit	10000	10000	100	0.359	0.006	20	0.022	0.0	random	NaN	20	NaN	0.288	0.008	1.249	1.249	See	See
9	KMeans_short	fit	10000	10000	100	1.206	0.015	20	0.007	0.0	k-means++	NaN	20	NaN	0.659	0.010	1.830	1.830	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter_sklearn	iteration_throughput	latency	init	adjusted_rand_score_sklearn	n_iter_sklearnex	adjusted_rand_score_sklearnex	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_short	predict	10000	1000	2	0.002	0.000	20	0.007	0.000	random	0.000	20	-0.001	0.001	0.0	3.515	3.526	See	See
2	KMeans_short	predict	10000	1	2	0.002	0.000	20	0.000	0.002	random	1.000	20	1.000	0.000	0.0	11.361	11.422	See	See
4	KMeans_short	predict	10000	1000	2	0.002	0.000	20	0.007	0.000	k-means++	0.001	20	-0.001	0.001	0.0	3.309	3.329	See	See
5	KMeans_short	predict	10000	1	2	0.002	0.000	20	0.000	0.002	k-means++	1.000	20	1.000	0.000	0.0	10.422	10.648	See	See
7	KMeans_short	predict	10000	1000	100	0.004	0.001	20	0.215	0.000	random	0.279	20	0.294	0.002	0.0	2.282	2.283	See	See
8	KMeans_short	predict	10000	1	100	0.002	0.000	20	0.000	0.002	random	1.000	20	1.000	0.000	0.0	8.500	8.832	See	See
10	KMeans_short	predict	10000	1000	100	0.004	0.001	20	0.195	0.000	k-means++	0.317	20	0.257	0.002	0.0	2.521	2.523	See	See
11	KMeans_short	predict	10000	1	100	0.002	0.000	20	0.000	0.002	k-means++	1.000	20	1.000	0.000	0.0	8.278	8.677	See	See

LogisticRegression

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

All estimators share the following parameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	class_weight	l1_ratio	n_jobs	random_state	accuracy_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	LogisticRegression	fit	1000000	1000000	100	17.705	0.406	[20]	0.045	0.000	NaN	NaN	NaN	NaN	NaN	3.136	0.018	5.646	5.646	See	See
3	LogisticRegression	fit	1000	1000	10000	1.579	0.651	[26]	0.051	0.002	NaN	NaN	NaN	NaN	NaN	1.304	0.036	1.211	1.212	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	class_weight	l1_ratio	n_jobs	random_state	accuracy_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	LogisticRegression	predict	1000000	1000	100	0.001	0.001	[20]	1.198	0.0	NaN	NaN	NaN	NaN	0.56	0.001	0.001	0.743	1.404	See	See
2	LogisticRegression	predict	1000000	1	100	0.000	0.000	[20]	0.009	0.0	NaN	NaN	NaN	NaN	1.00	0.000	0.000	0.353	0.355	See	See
4	LogisticRegression	predict	1000	100	10000	0.003	0.000	[26]	3.178	0.0	NaN	NaN	NaN	NaN	0.35	0.007	0.001	0.346	0.348	See	See
5	LogisticRegression	predict	1000	1	10000	0.000	0.000	[26]	0.623	0.0	NaN	NaN	NaN	NaN	0.00	0.002	0.000	0.065	0.065	See	See

Ridge

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

All estimators share the following parameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	max_iter	random_state	r2_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	Ridge	fit	1000	1000	10000	0.334	0.011	NaN	0.239	0.0	NaN	NaN	NaN	0.344	0.018	0.971	0.972	See	See
3	Ridge	fit	1000000	1000000	100	1.476	0.108	NaN	0.542	0.0	NaN	NaN	NaN	0.496	0.239	2.974	3.302	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	max_iter	random_state	r2_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	Ridge	predict	1000	1000	10000	0.012	0.001	NaN	6.661	0.0	NaN	NaN	0.083	0.02	0.001	0.587	0.588	See	See
2	Ridge	predict	1000	1	10000	0.000	0.000	NaN	0.833	0.0	NaN	NaN	NaN	0.00	0.000	0.647	0.665	See	See
4	Ridge	predict	1000000	1000	100	0.000	0.000	NaN	4.653	0.0	NaN	NaN	1.000	0.00	0.000	0.431	0.679	See	See
5	Ridge	predict	1000000	1	100	0.000	0.000	NaN	0.010	0.0	NaN	NaN	NaN	0.00	0.000	0.568	0.602	See	See